[Typing] 修改一些错误的类型标注以及示例代码 (#65452)

megemini · web-flow · commit d37cd92eefd1 · 2024-06-26T09:19:14.000+08:00
diff --git a/python/paddle/device/cuda/__init__.py b/python/paddle/device/cuda/__init__.py
@@ -164,7 +164,7 @@ def empty_cache():
             >>> import paddle
             >>> paddle.device.set_device('gpu')
 
-            >>> tensor = paddle.randn([512, 512, 512], "float")
+            >>> tensor = paddle.randn([512, 512, 512], "float64")
             >>> del tensor
             >>> paddle.device.cuda.empty_cache()
     '''
diff --git a/python/paddle/distribution/bernoulli.py b/python/paddle/distribution/bernoulli.py
@@ -149,7 +149,7 @@ def sample(self, shape):
                 >>> import paddle
                 >>> from paddle.distribution import Bernoulli
 
-                >>> rv = Bernoulli(paddle.full((1), 0.3))
+                >>> rv = Bernoulli(paddle.full([1], 0.3))
                 >>> print(rv.sample([100]).shape)
                 [100, 1]
 
@@ -205,7 +205,7 @@ def rsample(self, shape, temperature=1.0):
                 >>> paddle.seed(1)
                 >>> from paddle.distribution import Bernoulli
 
-                >>> rv = Bernoulli(paddle.full((1), 0.3))
+                >>> rv = Bernoulli(paddle.full([1], 0.3))
                 >>> print(rv.sample([100]).shape)
                 [100, 1]
 
diff --git a/python/paddle/framework/io.py b/python/paddle/framework/io.py
@@ -783,7 +783,7 @@ def save(obj, path, protocol=4, **configs):
             >>> paddle.save(layer_state_dict, "emb.pdparams")
 
             >>> scheduler = paddle.optimizer.lr.NoamDecay(
-            ...     d_model=0.01, warmup_steps=100, verbose=True)
+            ...     d_model=100, warmup_steps=100, verbose=True)
             >>> adam = paddle.optimizer.Adam(
             ...     learning_rate=scheduler,
             ...     parameters=emb.parameters())
@@ -1044,7 +1044,7 @@ def load(path, **configs):
             >>> paddle.save(layer_state_dict, "emb.pdparams")
 
             >>> scheduler = paddle.optimizer.lr.NoamDecay(
-            ...     d_model=0.01, warmup_steps=100, verbose=True)
+            ...     d_model=100, warmup_steps=100, verbose=True)
             >>> adam = paddle.optimizer.Adam(
             ...     learning_rate=scheduler,
             ...     parameters=emb.parameters())
diff --git a/python/paddle/nn/layer/container.py b/python/paddle/nn/layer/container.py
@@ -100,7 +100,7 @@ def __delitem__(self, key: str) -> None:
     def __len__(self) -> int:
         return len(self._sub_layers)
 
-    def __iter__(self) -> Iterator[Layer]:
+    def __iter__(self) -> Iterator[str]:
         return iter(self._sub_layers)
 
     def __contains__(self, key: str) -> bool:
diff --git a/python/paddle/static/nn/metric.py b/python/paddle/static/nn/metric.py
@@ -63,7 +63,7 @@ def accuracy(input, label, k=1, correct=None, total=None):
             >>> paddle.seed(2023)
             >>> paddle.enable_static()
             >>> data = static.data(name="input", shape=[-1, 32, 32], dtype="float32")
-            >>> label = static.data(name="label", shape=[-1,1], dtype="int")
+            >>> label = static.data(name="label", shape=[-1,1], dtype="int64")
             >>> fc_out = static.nn.fc(x=data, size=10)
             >>> predict = F.softmax(x=fc_out)
             >>> result = static.accuracy(input=predict, label=label, k=5)
@@ -196,7 +196,7 @@ def auc(
 
             >>> paddle.seed(2023)
             >>> data = paddle.static.data(name="input", shape=[-1, 32,32], dtype="float32")
-            >>> label = paddle.static.data(name="label", shape=[-1], dtype="int")
+            >>> label = paddle.static.data(name="label", shape=[-1], dtype="int64")
             >>> fc_out = paddle.static.nn.fc(x=data, size=2)
             >>> predict = paddle.nn.functional.softmax(x=fc_out)
             >>> result=paddle.static.auc(input=predict, label=label)
@@ -225,7 +225,7 @@ def auc(
 
             >>> paddle.seed(2023)
             >>> data = paddle.static.data(name="input", shape=[-1, 32,32], dtype="float32")
-            >>> label = paddle.static.data(name="label", shape=[-1], dtype="int")
+            >>> label = paddle.static.data(name="label", shape=[-1], dtype="int64")
             >>> ins_tag_weight = paddle.static.data(name='ins_tag_weight', shape=[-1,16], lod_level=0, dtype='float64')
             >>> fc_out = paddle.static.nn.fc(x=data, size=2)
             >>> predict = paddle.nn.functional.softmax(x=fc_out)
diff --git a/python/paddle/static/nn/sequence_lod.py b/python/paddle/static/nn/sequence_lod.py
@@ -223,7 +223,7 @@ def sequence_softmax(input, use_cudnn=False, name=None):
             >>> paddle.enable_static()
 
             >>> x = paddle.static.data(name='x', shape=[7, 1],
-                              dtype='float32', lod_level=1)
+            ...     dtype='float32', lod_level=1)
             >>> x_sequence_softmax_1 = paddle.static.nn.sequence_softmax(input=x)
 
             >>> y = paddle.static.data(name='y', shape=[7],
diff --git a/python/paddle/tensor/creation.py b/python/paddle/tensor/creation.py
@@ -1739,12 +1739,14 @@ def triu_(
 @overload
 def meshgrid(
     args: Sequence[paddle.Tensor], name: str | None = None
-) -> paddle.Tensor:
+) -> list[paddle.Tensor]:
     ...
 
 
 @overload
-def meshgrid(*args: paddle.Tensor, name: str | None = None) -> paddle.Tensor:
+def meshgrid(
+    *args: paddle.Tensor, name: str | None = None
+) -> list[paddle.Tensor]:
     ...
 
 
diff --git a/python/paddle/vision/models/vgg.py b/python/paddle/vision/models/vgg.py
@@ -16,7 +16,6 @@
 
 from typing import (
     TYPE_CHECKING,
-    Any,
     Literal,
     TypedDict,
 )
@@ -119,9 +118,7 @@ def forward(self, x: Tensor) -> Tensor:
         return x
 
 
-def make_layers(
-    cfg: dict[str, list[Any]], batch_norm: bool = False
-) -> Sequential:
+def make_layers(cfg: list[object], batch_norm: bool = False) -> Sequential:
     layers = []
     in_channels = 3
     for v in cfg:
diff --git a/python/paddle/vision/transforms/transforms.py b/python/paddle/vision/transforms/transforms.py
@@ -1604,23 +1604,35 @@ class RandomAffine(BaseTransform[_InputT, _RetT]):
             [3, 256, 300]
     """
 
-    degrees: Sequence[float]
-    translate: tuple[float, float] | None
-    scale: tuple[float, float] | None
-    shear: float | Sequence[float] | None
+    degrees: float | list[float] | tuple[float, float]
+    translate: list[float] | tuple[float, float] | None
+    scale: list[float] | tuple[float, float] | None
+    shear: (
+        float
+        | list[float]
+        | tuple[float, float]
+        | tuple[float, float, float, float]
+        | None
+    )
     interpolation: _InterpolationPil | _InterpolationCv2
     fill: Size3
-    center: tuple[float, float]
+    center: list[float] | tuple[float, float]
 
     def __init__(
         self,
-        degrees: float | Sequence[float],
-        translate: tuple[float, float] | None = None,
-        scale: tuple[float, float] | None = None,
-        shear: float | Sequence[float] | None = None,
+        degrees: float | list[float] | tuple[float, float],
+        translate: list[float] | tuple[float, float] | None = None,
+        scale: list[float] | tuple[float, float] | None = None,
+        shear: (
+            float
+            | list[float]
+            | tuple[float, float]
+            | tuple[float, float, float, float]
+            | None
+        ) = None,
         interpolation: _InterpolationPil | _InterpolationCv2 = 'nearest',
         fill: Size3 = 0,
-        center: tuple[float, float] = None,
+        center: list[float] | tuple[float, float] = None,
         keys: _TransformInputKeys | None = None,
     ) -> None:
         self.degrees = _setup_angle(degrees, name="degrees", req_sizes=(2,))
